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Reliability Analysis Considering Tail Dependence over Space and Time

机译：考虑尾部依赖空间和时间的可靠性分析

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摘要

Tail dependence is important for reliability analysis since failure events are usually located at the tail of distributions. The common practice of multivariate ARMA model and Karhunen-Loeve (KL) expansion method for the modeling of multivariate stochastic loads cannot capture the tail dependence between loads. This paper presents a combination of Vine copula and autoregressive-moving average (ARMA) for load modeling in time-dependent reliability analysis, in problems with a vector of correlated non-Gaussian stochastic loads. Univariate ARMA models are used to maintain the correlation of stochastic loads over time. The vine-copula model can account for not only correlations between different univariate ARMA models, but also the tail dependence of different ARMA models. The proposed Vine-ARMA model is able to flexibly model a vector of high-dimensional correlated non-Gaussian stochastic processes with the consideration of tail dependence. In order to overcome the challenges in computational effort, a recently developed single-loop Kriging (SILK) surrogate modeling method is used to efficiently and accurately perform reliability analysis. A hydrokinetic turbine blade subjected to a vector of stochastic river flow loads is used to demonstrate the effectiveness of the proposed reliability analysis method.
机译：尾部依赖对于可靠性分析很重要，因为失败事件通常位于分布尾部。多变量ARMA模型和Karhunen-Loeve（KL）扩展方法的常见做法是用于模拟多变量随机载荷的模型不能捕获负载之间的尾部依赖性。本文介绍了藤蔓和自回归移动平均（ARMA）的载荷建模，以时间依赖性可靠性分析，在相关的非高斯随机载荷矢量中存在的问题。单变量ARMA模型用于保持随机负荷随时间的相关性。 vine-copula模型不仅可以解释不同的单变量arma模型之间的相关性，也可以占不同arma模型的尾部依赖性。所提出的vine-arma模型能够灵活地模拟高维相关的非高斯随机过程的向量，考虑到尾部依赖性。为了克服计算工作中的挑战，最近开发的单环克里格（丝绸）代理建模方法用于有效准确地进行可靠性分析。经过随机河流载荷载体的载体的水动力涡轮叶片用于证明所提出的可靠性分析方法的有效性。

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